Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT07620301

LLM Intervention for Tobacco in Underserved Populations (LIFT-UP)

This study will test a tailored, multilingual tobacco cessation chatbot called LIFT-UP (LLM Intervention for Tobacco in Underserved Populations), designed to better meet the needs of people living in persistent poverty census tracts.

This study will use 1:1 semi-structured interviews to explore social drivers of health impacting TC, as well as digital access and preferences among those living in PPCTs. This qualitative approach enables guided yet flexible exploration of key domains while capturing unanticipated insights relevant to refining the chatbot.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Huntsman Cancer Institute/ University of Utah

Salt Lake City, Utah, 84102, United States

Location contact

Christian Mahony Reategui Rivera, MD, MS

PRINCIPAL_INVESTIGATOR

Lindsey Potter, MPH, PhD

CONTACT

[email protected]

801-213-6036

Lindsey Potter, MPH, PhD

PRINCIPAL_INVESTIGATOR

About this study

Tobacco use is a major cause of cancer and is responsible for about half a million deaths in the United States each year. Because of this, helping people stop using tobacco is one of the most important ways to prevent cancer. Although tobacco use has decreased over time, many adults in the U.S. still use tobacco. Many people try to quit each year, but most quit attempts are not successful. One reason is that many people do not use proven, evidence-based quit support, such as counseling or quit medications.

People who live in areas with long-term poverty often face additional barriers that can make quitting harder. These areas may have fewer job and education opportunities, limited access to healthcare and community resources, and higher levels of day-to-day stress (for example, related to financial strain or lack of health insurance). People with lower income are just as likely to try to quit as those with higher income, but they are less likely to quit successfully and are less likely to use evidence-based quitting support. Many persistent poverty areas are also rural and have higher numbers of people who prefer to speak languages other than English, including Spanish, which creates an additional need for bilingual and culturally appropriate quit support.

Digital tools may help increase access to evidence-based tobacco cessation support in these communities. Mobile phone ownership is very common, including among people with lower incomes. However, some smartphone apps require reliable internet access or data plans, which can be a barrier. Text messaging is accessible on nearly all phones, does not require internet access, can be offered in multiple languages, and can be tailored to the needs of the user.

Text-based programs that use artificial intelligence (AI), such as large-language-model chatbots, may be especially useful because they can provide interactive support using natural language and can be delivered at scale. Chatbots have been used successfully in other areas of health, but many existing programs use fixed scripts and may not feel relevant or helpful for all groups. Importantly, most tobacco cessation chatbots have not been designed to address barriers faced by people living in persistent poverty areas.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 18+ years old
  • Use ≥3 cigarettes/day on average
  • Motivated to quit in the next 30 days
  • Have a computer or tablet with internet access for 1:1 interviews
  • Speak English or Spanish
  • Home address is in an area characterized by persistent poverty

Exclusion criteria

  • None

Treatment and study plan

LIFT-UP Chatbot

Behavioral

LIFT-UP Chatbot will be developed, evaluated, and refined using GARDE-Chat, an open-source chatbot authoring platform that has been used to support the development of chatbot-based interventions tested in large pragmatic clinical trials.

Primary outcomes

  1. System Usability Scale (SUS)

    Time frame: up to 1 day

    Usability will be measured using the SUS, a questionnaire assessing the perceived usability of a system, product, website, app, or digital intervention. It consists of ten 5-point Likert items ranging from "Strongly disagree" to "Strongly agree".

    Scoring follows the standard SUS scoring procedure, for positively worded items, the item score is calculated as response minus 1; for negatively worded items, the item score is calculated as 5 minus the response. The 10 item scores are summed and then multiplied by 2.5 to generate the final SUS score, with higher scores indicating greater perceived usability. Score range: 0-100.

Secondary outcomes

  1. Usability - Chat Bot Usability Scale (BUS-11)

    Time frame: up to 1 day

    Chatbot usability will be measured using the Chatbot Usability Scale (BUS-11). BUS-11 is a measured that assesses users' experiences after interacting with a chatbot or conversational agent. The BUS-11 consists of eleven 5-point Likert items ranging from "Strongly disagree" to "Strongly agree". Each item is coded from 1 to 5, and item scores are summed to create a total score. Higher scores indicate greater perceived chatbot usability. Score range: 11-55.

  2. Acceptability

    Time frame: up to 1 day

    Acceptability will be measured using the Acceptability of Intervention Measure (AIM). AIM is an instrument that assesses the perceived acceptability of an intervention. It consists of four 5 point Likert items ranging from "Completely "disagree" to "Strongly agree". Each item is coded from 1 to 5, and the overall score is the mean of the items score. Higher scores indicate greater perceived acceptability of the intervention. Score range: 1-5.

  3. Digital Working Alliance

    Time frame: up to 1 day

    Working alliance in the digital context will be measured with the Digital Working Alliance inventory (D-WAI). D-WAI is derived from the Working Alliance Inventory and measures the perceived working alliance (e.g., traditionally the collaborative bond between therapist and client) with digital interventions. It includes six 7-point Likert items ranging from "Strongly disagree" to "Strongly agree". Each item is coded from 1 to 7, and item scores are summed to create a total score. Higher scores indicate a stronger perceived digital working alliance. Score range: 7-42.

  4. Perceived cultural fit

    Time frame: up to 1 day

    Perceived cultural relevance will be measured using the Cultural Relevance Questionnaire (CRQ). CRQ consists of six 5-point Likert items ranging from "Strongly disagree" to "Strongly agree. Higher scores indicate greater perceived cultural appropriateness/relevance of the intervention. Score range: 5-25. An additional 5-point Likert-like question was added to reflect overall cultural fit perceived by the users.

Study contacts

Contact information is provided by the study sponsor or research team.

Lindsey Potter, MPH, PhD

CONTACT

[email protected]

801-213-6036

Sponsors and collaborators

Lead sponsor

University of Utah

Other

Collaborators

  • Huntsman Cancer Institute
  • National Cancer Institute (NCI)

Registry information

Acronym: LIFT-UP

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jun 2, 2026
Registry last updated
Jun 3, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.